The invention relates generally to Demand Response. More specifically, the invention relates to a method that generates load patterns from historical customer consumption data, and generates customer loads from the generated load patterns and price responses from given economic parameters.
Demand Response (DR) has become an important topic in recent years. Soaring fuel prices coupled with the environmental need to reduce fuel consumption has directed the players in the electricity sector to search for new ways to manage the generation, transmission and distribution process more efficiently. With the introduction of smart power metering systems to a mass number of customers, real-time monitoring of customer loads is made possible.
In order to employ DR mechanisms, methods such as machine learning algorithms are needed to interpret results. Yet, these methods require real world data to be calibrated, which is not readily available. Moreover, obtaining such data requires serious investment, pilot DR studies and in-depth econometric analyses.
What is desired is a method to effectively and efficiently interpret customer response and manage it, so that the overall process from electricity generation to consumption is more desirable for both the utility companies and the consumers. The utilities benefit as they can avoid costly levels of peak electrical load, while consumers enjoy lowered bills as a result of their participation.